long-run covariability - princeton university
TRANSCRIPT
Motivation
• Study the long-run covariability/relationship between economic variables
⇒ great ratios, long-run Phillips curve, nominal exchange rates and relativeprice levels, etc.
• Challenge: many economic time series are persistent
⇒ spurious regression effects
• Cointegration framework is highly constraining
⇒ Very specific model of persistence, rigid relationship between persistenceand long-run covariability
1
This Paper
• Defines population measures about long-run covariability of general bivari-ate process
• Derives confidence intervals about these measures that are valid in flexibleparametric model of long-run properties
⇒ “Descriptive statistics with confidence intervals”
• Statistical framework reflects sparsity of long-run information (cf. Müllerand Watson (2008, 2016a, 2016b))
⇒ No consistent estimation of long-run properties
⇒ Inference in presence of nuisance parameters (Elliott, Müller and Watson(2015), Müller and Norets (2016))
2
Long-Run Correlation Matrix
× TFP 091∗ 053∗ 098∗ 078∗
90% CI [071; 097] [002; 081] [095; 099] [045; 095]
053∗ 092∗ 070∗
90% CI [003; 081] [068; 097] [028; 091]
051∗ 03890% CI [002; 080] [−008; 071]
× 072∗
90% CI [038; 093]
3
Outline of Talk
1. Introduction
2. Statistical framework, running examples
3. Measuring long-run covariability
4. A flexible parametric model of long-run properties
5. Construction of confidence intervals
6. Additional applications
4
Cosine Transforms and Long-Run Projections
• Let Ψ() =√2 cos()
• For a scalar sequence =1, define
=1√
X=1
Ψ( )
=
√2√
X=1
cos( ), = 1 · · ·
8
Long-Run Projections
• Define as the predicted values from a regression of on Ψ( )=1
• Equivalently
=1√
X=1
Ψ( ) =
√2√
X=1
cos( )
because the weights Ψ( ) are orthonormal
• usefully thought of as low-pass filter for frequencies lower than 2periods (66 years of data, = 12 ⇒ lower than 11 year cycles)
• fully characterized by regression coefficients
12
Asymptotic Behavior of Cosine Transforms
• Let ( 0
0 )0 be 2 × 1 cosine transforms of ( )=1
• Parameters of interest and suggested confidence intervals are defined interms of (0
0 )0
• Consider asymptotics where is fixed, as in Müller (2004, 2007, 2014),Phillips (2006), Müller and Watson (2008, 2013, 2016a, 2016b), Sun(2013, 2016)
⇒ Defines notion of “long-run”: Periods of interest are 2 and longer
⇒ Reflects that in samples of interest, reasonable are small
⇒ Ignoring data information beyond (0
0 )0 avoids modelling higher
frequency properties
15
Asymptotic Behavior of Cosine Transforms
• Assume (∆∆) stationary with a spectral density . Let () = ()|1− i|2 be the pseudo-spectrum of ( ), and suppose
( )→ ()
in a suitable sense.
• Under linear process assumption and additional regularity conditions, byCLT in Müller and Watson (2016)Ã
!⇒
Ã
!∼ N (0Σ) , Σ =
ÃΣ ΣΣ Σ
!and
Σ = Var
Ã
!→ Σ
where Σ is a function of .16
Measuring Long-Run Covariability
Ω = −1X=1
⎡⎣Ã
!Ã
!0⎤⎦ = X=1
⎡⎣Ã
!Ã
!0⎤⎦=
ÃtrΣ trΣtrΣ trΣ
!→
ÃtrΣ trΣtrΣ trΣ
!
=
ÃΩ ΩΩ Ω
!= Ω
• Scalar parameters derived from Ω
— = Ω √ΩΩ
— = Ω Ω
— =qΩ −Ω2 Ω
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Interpretation of and
• Define
= argmin
⎡⎣−1 X=1
( − )2
⎤⎦ = argmin
⎡⎣ X=1
( − )2
⎤⎦ =
vuuutmin
⎡⎣−1 X=1
( − )2
⎤⎦ =vuuutmin
⎡⎣ X=1
( − )2
⎤⎦
• Then → and → , since
⎡⎣−1 X=1
( − )2
⎤⎦ = −1
⎡⎣ X=1
2 − 2X=1
+ 2X=1
2
⎤⎦= Ω − 2Ω + 2Ω
• Population 2 is Ω2(ΩΩ )→ 2
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Comparison with Standard Spectral Analysis
• Recall that is pseudo-spectrum of ( ), and ( )→ ()
• Up to reasonable approximation, Ω is to average over (pseudo) spec-tral density () over frequencies ∈ [ ], corresponding toaverage of () over ∈ [ ].
• Two departures from classic spectral analysis:
1. Derive inference for fixed, no LLN applicable
2. Allow for curvature in that is non-negligble in 1 neighborhood(=don’t assume is flat).
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Parametrizing , and Implied Ω
Two important special cases:
1. In I(0) model, () ∝ Λ, ( )0 ∼ N (0Λ)
⇒ Ω ∝ Λ
2. In I(1) model, () ∝ Λ2, ( )0 ∼ N (0Λ2)
⇒ Ω ∝ Λ
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I(0) and I(1) Inference
⇒ Follows from well-known small sample results (cf. Anderson (1984))
and I-RatesI(0) I(1) I(0) I(1)
Estimates 0.93∗ 0.93∗ 0.97∗ 0.94∗
90% CI [0.81;0.97] [0.82;0.97] [0.93;0.99] [0.82;0.97]
Estimates 0.76∗ 0.84∗ 0.96∗ 0.85∗
90% CI [0.60;0.92] [0.67;1.01] [0.84;1.08] [0.68;1.03]
Estimates 0.35 0.35 0.63 0.4890% CI [0.26;0.55] [0.26;0.54] [0.47;0.97] [0.36;0.74]
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A Flexible Parametric Model for
• ( ) model:
() =
Ã(2 + 21)
−1 0
0 (2 + 22)−2
!0 +0
with ∈ [−04 1], ∈ [0∞] and lower triangular
• 11 parameter model that embeds bivariate fractional, local-to-unity andlocal-level models as special cases
• Allows for various long-run phenomena such as (stochastic) breaks inmeans, slow mean reversion, overdifferencing, etc.
• Ω can be computed from implied Σ = Σ(), = ( )
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Construction of Confidence Intervals
• Under asymptotic approximation, a parametric small sample problem:
— Observe = ( 0 0)0 ∼ N (0Σ), where Σ = Σ(), =
( ) ∈ Θ
— Seek confidence interval () for parameter of interest = (), forknown
— Impose appropriate invariance on
• -weighted average expected length minimizing program
min
Z[lgth(())] () s.t. (() ∈ ()) ≥ 1− ∀ ∈ Θ
⇒ Optimal depends on unknown Lagrange multipliers
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Form of Optimal Confidence Set
• For simplicity, ignore invariance. Then (cf. Pratt 1961), optimal invertstests 0 of 0 : () = 0 of the form
0() = 1[Z() () cv
Z()Λ0()]
for some probability distribution Λ0 on Θ with support on a subset of : () = 0, Λ0( : (() ∈ ()) 1 − ) = 0 and[0()] ≤ for all () = 0
• Similar form under invariance, but not a family of distributions Λ if para-meter of interest is affected by invariance
⇒ see Müller and Norets (2016) for details. Effective dimension of para-meter space after invariance is 8 for all three problems.
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Computation of Λ
• Arbitrary Λ induces lower bound on expected length criterion that holdsfor all valid CIs (cf. Elliott, Müller and Watson (2015))
• Basic algorithm
1. Let Θ = 1 be candidate set for support of Λ
2. Compute Λ weights and cv such that size of test is − on Θ.
3. Check size control on Θ:
(a) If size is controlled, we are done (compare length to bound generatedfrom cv0 that induces Λ-weighted size equal to ).
(b) If size violated, add violating to Θ and go to Step 2.
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Computational Details
• Λs have about 30-100 points of support
• CS are within 5% of lower bound on length criterion for = 01
• Size control: 500 consecutive BFGS searches with random starting valuesdon’t find violation
• Single problem takes about 30 minutes on fast PC using Fortran
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Choice of Weighting Function
• Recall
() =
Ã(2 + 21)
−1 0
0 (2 + 22)−2
!0 +0
• Set = 0, 1 = 2 = 0, 1 2 ∼ [−04 14],
∼ (1) diag(150 1) (2)
where ∼ [0 1] and () is 2× 2 rotation matrix of angle .
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Credibility of Resulting CS
• Optimal () could be empty for some , or unreasonably short
⇒ Perversely, this is optimal for very uninformative draws , as coveragethen costly in terms of length
• Generic problem in nonstandard problems: Frequentist (optimality) prop-erties don’t rule out unreasonable descriptions of uncertainty for somerealizations
⇒ Analyzed in detail in Müller and Norets (2016), building on old literature(Fisher (1956), Buehler (1959), Wallace (1959), Cornfield (1969), Pierce(1973), Robinson (1977), etc.)
• Suggested solution: constrained to be superset of equal-tailed credibleset relative to prior
30
Spurious Regression?
• Minimal coverage of weighted average length minimizing nominal 90% CIsfor under = 0
DGP\Assumed Model I(0) I(1) I(0) 0.90 0.01 0.91I(1) 0.01 0.90 0.90 0.01 0.00 0.90
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Results in Running Examples
I(0) I(1) and
Estimates 0.93∗ 0.93∗ 0.91∗
90% CI [0.81;0.97] [0.82;0.97] [0.71;0.97]
Estimates 0.76∗ 0.84∗ 0.76∗
90% CI [0.60;0.92] [0.67;1.01] [0.48;0.95]
Estimates 0.35 0.35 0.4090% CI [0.26;0.55] [0.26;0.54] [0.27;0.66]
I-Rates Estimates 0.97∗ 0.94∗ 0.96∗
90% CI [0.93;0.99] [0.82;0.97] [0.89;0.99]
Estimates 0.96∗ 0.85∗ 0.92∗
90% CI [0.84;1.08] [0.68;1.03] [0.75;1.14]
Estimates 0.63 0.48 0.7090% CI [0.47;0.97] [0.36;0.74] [0.53;0.92]
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Real Variables: Correlation
× TFP 091∗ 053∗ 098∗ 078∗
90% CI [071; 097] [002; 081] [095; 099] [045; 095]
053∗ 092∗ 070∗
90% CI [003; 081] [068; 097] [028; 091]
051∗ 03890% CI [002; 080] [−008; 071]
× 072∗
90% CI [038; 093]
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